As the need to provide automated responses to text-based or audio-based questions grows, many automated chat services have appeared on the market.
A rather common problem in spoken or textual answers is the problem of analogies such as “What is the relationship between Little Ming and Little Hong?”
At present, chatbots generally answer analogical questions by deriving the same-class or analogical relationship between two entities on the basis of RDF (Resource Description Framework).
Given that the inter-entity relationship is sought on the basis of an RDF knowledge base, it is necessary to construct a complete RDF knowledge base in advance.
The construction of an RDF knowledge base generally requires three steps that are iteratively performed. For example, the three steps of constructing an RDF knowledge base comprise: uncovering relationship templates through mining, cleaning an encyclopedic range of data, and extracting relationships. This work expends large amounts of effort and physical resources and is also costly. Yet, the coverage is limited and consequently, the success rates of responses to analogical questions are low.
For example, the following is stated in a piece of captured online content: “Andy Lau and Jackie Chan are close friends.” Thus, this is what is recorded in the RDF knowledge base: Andy Lau, Jackie Chan, relationship close friends, and other such information.
If an automated chat service receives the question “What is the relationship between Andy Lau and Jackie Chan?” from a user, the automated chat service will find in the RDF knowledge base that the relationship is “close friends” and will answer “close friends.”
However, if the automated chat service has not previously captured this piece of content, it will be unable to respond and might avoid the question by answering with a default response, such as, “What is the relationship?”
In addition, RDF-based responses are in a question and answer form. In a chat system, it might not be possible for an automated chat service to come up with an answer. Furthermore, typical automated chat services can provide answers, but typically in a terse and rigid form that appears robotic. A user is more likely to engage in the service longer if the responses are in more natural, human language, such as language that expresses humor or mimics a human's thought process.